How much VRAM do I need for nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF Q2_S?

You need 11.87 GB of VRAM to run nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF at Q2_S quantization. The model file is 9.38 GB with a context window of 8,192 tokens.

Volker Mauel Q2_S 30B Parameters

VRAM Requirements for
nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF Q2_S

To run nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF locally at Q2_S quantization, you need at minimum 11.87 GB of GPU VRAM.

11.87 GB

Required VRAM

9.38 GB

File Size

8K tokens

Context Window

30B

Parameters

Estimated VRAM Required

11.87

Mid-Range GPU Required

0 8GB
RTX 3060 16GB
RTX 3080 24GB
RTX 4090 48GB
A6000 80GB
A100 80GB+

Recommended GPU Configurations

⚠️ 1.9 GB short

Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (Q2_S) 9.38 GB

Context Overhead (8,192 tokens × 30B × 2 ÷ 1M) 0.492 GB

System Buffer (OS + CUDA runtime) 2.00 GB

Total Required VRAM 11.87 GB

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Use the interactive calculator to compare nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF across all available formats.

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Frequently Asked Questions

Can I run nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF Q2_S on a consumer GPU?

Yes! At 11.87 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.

What happens if I don't have enough VRAM?

If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.

Can I use multiple GPUs to run nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF?

Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.

Is Q2_S quality good enough for production?

Q2_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF Q2_S

Recommended GPU Configurations

RTX 3080 (10GB)

RTX 4070 Ti (12GB)

RTX 4090 (24GB)

📊 VRAM Calculation Breakdown

Try a Different Quantization

nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF — Other Quantizations

Frequently Asked Questions

VRAM Requirements for
nebius-SWE-rebench-openhands-Qwen3-30B-A3B-GGUF Q2_S